It is important for wireless communication systems to suppress interference in received signals in order to make efficient use of limited spectrum resources. One example of the technique for improving the efficiency in spectrum use is an interference canceller based on maximum likelihood sequence estimation (MLSE) shown in FIG. 1. This interference canceller, named a MLSE-based interference canceller, generates a replica of the received signal to substantially remove interference. See, for example, “Interference Canceller for Signals with Different Symbol Rates”, Proc. IEICE General Conference, B-5-174, March, 2004, which publication is referred to as “Publication 1”.
With the MLSE-based interference canceller shown in FIG. 1, a channel estimator “a” successively estimates the states of channels of a desired and an interference signals using an estimation error and a reference signal. A desired signal replica generator “b” and an interference signal replica generator “c” generate the replicas of the desired and the interference signals, respectively, for all possible symbol sequence candidates of the desired and the interference signals by performing convolution of the symbol sequence candidates with the associated channel estimation values. The desired signal replica and the associated interference signal replica are added to produce a received signal replica. A maximum likelihood sequence estimator “d” selects a pair of symbol sequence candidates of the desired and the interference signals, whose received signal replica is closest to the actual received signal, and outputs the selected symbol sequence candidate of the desired signal as a decision result of the received signal. In this manner, the interference is substantially eliminated. A known symbol sequence is used as the reference signal in the training section, and the decided symbol sequence is used as the reference signal in the data section.
By adaptively removing the interference signal from the received signal, different signals can use the same frequency at the same time, and consequently, frequency utilization efficiency can be improved.
Another known technique for improving the frequency utilization efficiency is successive multi-user detection shown in FIG. 2. In FIG. 2, multi-user detection is performed using a minimum mean square error (MMSE) filtering technique with respect to multiple user signals with the same signal bandwidth. See “An Efficient Square-root Algorithm for BLAST”, International Conference on Acoustics, Speech and Signal Processing (ICASSP), June, 2000, which publication is referred to as “Publication 2”.
In FIG. 2, multi-user detection is performed using MMSE filters to detect multiple user signals with the same bandwidth. The process is focused on the first detection target signal (the signal to be detected k-th is referred to as the “k-th detection target signal”). The first detection target signal is equalized using the MMSE filter #1 making use of channel state information of all the detection target signals estimated and held in advance. Based on the equalized signal, signal detection and replica generation for the first detection target signal are performed. Then, the process is focused on the second detection target signal to perform equalization, signal detection and replica generation, using a residual signal obtained by subtracting the replica of the first detection target signal from the input signal. Accordingly, interference from the first detection target signal has been reduced when carrying out signal detection of the second detection target signal, and a reliable detection result can be obtained. The k-th detection target signal is processed using a residual signal obtained by subtracting the replicas of the first through (k−1)th detection target signals from the input signal.
By successive detection and removal of the replicas of other signals that are interference sources to the target signal, various-types of signals can use the same frequency at the same time, whereby the frequency utilization efficiency is improved.
The MLSE-based interference canceller shown in FIG. 1 is capable of signal detection through generation of replicas of the signal components contained in the received signal. However, if the number of signals to be processed is increased, the complexity increases exponentially, and it becomes difficult to complete the process within a realistic processing time. Especially, when many narrow-band signals are overlaid on a broadband signal and transmitted together at the same frequency band, the multiple narrow-band signals have to be processed at the same time in order to detect the broadband signal, which makes the process more difficult.
With the MMSE filtering based multi-user detection shown in FIG. 2, signal equalization is performed at the MMSE filter using channel state information of not only the desired signal but also the interference signal, and therefore, the signal detection accuracy and the precision of replica generation can be maintained high. In general, the used channel state information is estimated on the receiving side. However, when multiple signals with different signal bandwidths are overlaid at the same frequency band, each signal is bandlimited by a filter (not shown) with a passband different from the transmission side, and therefore, the sampled signal is affected by the influence of inter-symbol interference (ISI). The inter-symbol interference for a detection target signal may not occur when using a conventional bandlimiting filter in an environment without delay waves because the pass-bands of the transmission-side bandlimiting filter and the receiving-side bandlimiting filter are the same for the detection target signal itself contained in the received signal. However, for the other user signals with signal bandwidths different from that of the detection target signal, the received signal is band-limited by the receiving-side bandlimiting filter for the detection target signal, and therefore, the pass-bands of the transmission-side bandlimiting filter and the receiving-side bandlimiting filter are different from each other. In this state, inter-symbol interference occurs. The inter-symbol interference varies greatly depending on the sampling timing, and then the channel state information, which includes the effects of bandlimiting filter, also fluctuates greatly depending on the sampling timing. Such fluctuations may be estimated by channel estimation using a fractional tap-spacing coefficient variable filter. See “Fractional Tap-Spacing Equalizer and Consequences for Clock Recovery in Data Modems,” IEEE Transaction on Communications, August, 1976, which publication is referred to as “Publication 3”. However, with this method, the number of filter taps is relatively large, and consequently, computational workload increases and degradation in channel estimation accuracy becomes conspicuous.